Rheumatoid Arthritis Associated Episcleritis and Scleritis: An Update on Treatment Perspectives
Bibliographic record
Abstract
Episcleritis and scleritis are the most common ocular inflammatory manifestation of rheumatoid arthritis. Rheumatoid arthritis (RA) accounts for 8% to 15% of the cases of scleritis, and 2% of patients with RA will develop scleritis. These patients are more likely to present with diffuse or necrotizing forms of scleritis and have an increased risk of ocular complications and refractory scleral inflammation. In this review we provide an overview of diagnosis and management of rheumatoid arthritis-associated episcleritis and scleritis with a focus on recent treatment perspectives. Episcleritis is usually benign and treated with oral non-steroidal anti- inflammatory drugs (NSAIDs) and/or topical steroids. Treatment of scleritis will classically include oral NSAIDs and steroids but may require disease-modifying anti-rheumatic drugs (DMARDs). In refractory cases, treatment with anti TNF biologic agents (infliximab, and adalimumab) is now recommended. Evidence suggests that rituximab may be an effective option, and further studies are needed to investigate the potential role of gevokizumab, tocilizumab, abatacept, tofacitinib, or ACTH gel. A close cooperation is needed between the rheumatology or internal medicine specialist and the ophthalmologist, especially when scleritis may be the first indicator of an underlying rheumatoid vasculitis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".